



"""simple docstring"""





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

lowercase					=       random.Random()




def 						UpperCAmelCase  (		A						:    str  ,					A						:    int=1.0  ,					A						:    List[Any]=None  ,					A						:    Optional[int]=None							):

		'''simple docstring'''


		if rng is None:
				_UpperCAmelCase							=		global_rng

		_UpperCAmelCase							=		[]
		for batch_idx in range(shape[0]							):
				values.append([]							)
				for _ in range(shape[1]							):
						values[-1].append(rng.random() * scale							)

		return values




@require_torch
@require_torchaudio
class       lowercase__      (   unittest.TestCase     ):
							'''simple docstring'''







							def __init__(	self						,							snake_case						,							snake_case=7						,							snake_case=400						,							snake_case=2000						,							snake_case=24						,							snake_case=24						,							snake_case=0.0						,							snake_case=16000						,							snake_case=True						,							snake_case=True						,							)      ->				Optional[int]:
									_UpperCAmelCase							=		parent
									_UpperCAmelCase							=		batch_size
									_UpperCAmelCase							=		min_seq_length
									_UpperCAmelCase							=		max_seq_length
									_UpperCAmelCase							=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									_UpperCAmelCase							=		feature_size
									_UpperCAmelCase							=		num_mel_bins
									_UpperCAmelCase							=		padding_value
									_UpperCAmelCase							=		sampling_rate
									_UpperCAmelCase							=		return_attention_mask
									_UpperCAmelCase							=		do_normalize






							def 							lowerCamelCase_ (	self    )      ->				int:
									return {
									    "feature_size": self.feature_size,
									    "num_mel_bins": self.num_mel_bins,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									    "do_normalize": self.do_normalize,
									}






							def 							lowerCamelCase_ (	self						,							snake_case=False						,							snake_case=False    )      ->				Union[str, Any]:


									def _flatten(snake_case    ):
											return list(itertools.chain(*lowerCAmelCase__    )    )

									if equal_length:
											_UpperCAmelCase							=		[floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
									else:
											# make sure that inputs increase in size
											_UpperCAmelCase							=		[
											    floats_list((x, self.feature_size)    )
											    for x in range(self.min_seq_length						,							self.max_seq_length						,							self.seq_length_diff    )
											]
									if numpify:
											_UpperCAmelCase							=		[np.asarray(lowerCAmelCase__    ) for x in speech_inputs]
									return speech_inputs







@require_torch
@require_torchaudio
class       lowercase__      (   _UpperCAmelCase,       unittest.TestCase     ):
							'''simple docstring'''







							_UpperCAmelCase   					=       SpeechaTextFeatureExtractor if is_speech_available() else None






							def 							lowerCamelCase_ (	self    )      ->				Union[str, Any]:
									_UpperCAmelCase							=		SpeechaTextFeatureExtractionTester(self    )






							def 							lowerCamelCase_ (	self						,							snake_case    )      ->				List[str]:
									self.assertTrue(np.all(np.mean(lowerCAmelCase__						,							axis=0    ) < 1E-3    )    )
									self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__						,							axis=0    ) - 1    ) < 1E-3    )    )






							def 							lowerCamelCase_ (	self    )      ->				str:
									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									# create three inputs of length 800, 1000, and 1200
									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in range(800						,							1400						,							200    )]
									_UpperCAmelCase							=		[np.asarray(lowerCAmelCase__    ) for speech_input in speech_inputs]

									# Test feature size
									_UpperCAmelCase							=		feature_extractor(lowerCAmelCase__						,							padding=lowerCAmelCase__						,							return_tensors='np'    ).input_features
									self.assertTrue(input_features.ndim == 3    )
									self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size    )

									# Test not batched input
									_UpperCAmelCase							=		feature_extractor(speech_inputs[0]						,							return_tensors='np'    ).input_features
									_UpperCAmelCase							=		feature_extractor(np_speech_inputs[0]						,							return_tensors='np'    ).input_features
									self.assertTrue(np.allclose(lowerCAmelCase__						,							lowerCAmelCase__						,							atol=1E-3    )    )

									# Test batched
									_UpperCAmelCase							=		feature_extractor(lowerCAmelCase__						,							return_tensors='np'    ).input_features
									_UpperCAmelCase							=		feature_extractor(lowerCAmelCase__						,							return_tensors='np'    ).input_features
									for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__						,							lowerCAmelCase__    ):
											self.assertTrue(np.allclose(lowerCAmelCase__						,							lowerCAmelCase__						,							atol=1E-3    )    )

									# Test 2-D numpy arrays are batched.
									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in (800, 800, 800)]
									_UpperCAmelCase							=		np.asarray(lowerCAmelCase__    )
									_UpperCAmelCase							=		feature_extractor(lowerCAmelCase__						,							return_tensors='np'    ).input_features
									_UpperCAmelCase							=		feature_extractor(lowerCAmelCase__						,							return_tensors='np'    ).input_features
									for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__						,							lowerCAmelCase__    ):
											self.assertTrue(np.allclose(lowerCAmelCase__						,							lowerCAmelCase__						,							atol=1E-3    )    )






							def 							lowerCamelCase_ (	self    )      ->				int:
									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in range(800						,							1400						,							200    )]

									_UpperCAmelCase							=		['longest', 'max_length', 'do_not_pad']
									_UpperCAmelCase							=		[None, 16, None]
									for max_length, padding in zip(lowerCAmelCase__						,							lowerCAmelCase__    ):
											_UpperCAmelCase							=		feature_extractor(
											    lowerCAmelCase__						,							padding=lowerCAmelCase__						,							max_length=lowerCAmelCase__						,							return_attention_mask=lowerCAmelCase__    )
											_UpperCAmelCase							=		inputs.input_features
											_UpperCAmelCase							=		inputs.attention_mask
											_UpperCAmelCase							=		[np.sum(lowerCAmelCase__    ) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )






							def 							lowerCamelCase_ (	self    )      ->				List[str]:
									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in range(800						,							1400						,							200    )]

									_UpperCAmelCase							=		['longest', 'max_length', 'do_not_pad']
									_UpperCAmelCase							=		[None, 16, None]
									for max_length, padding in zip(lowerCAmelCase__						,							lowerCAmelCase__    ):
											_UpperCAmelCase							=		feature_extractor(
											    lowerCAmelCase__						,							max_length=lowerCAmelCase__						,							padding=lowerCAmelCase__						,							return_tensors='np'						,							return_attention_mask=lowerCAmelCase__    )
											_UpperCAmelCase							=		inputs.input_features
											_UpperCAmelCase							=		inputs.attention_mask
											_UpperCAmelCase							=		[np.sum(lowerCAmelCase__    ) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6    )
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6    )
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )






							def 							lowerCamelCase_ (	self    )      ->				Union[str, Any]:
									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in range(800						,							1400						,							200    )]
									_UpperCAmelCase							=		feature_extractor(
									    lowerCAmelCase__						,							padding='max_length'						,							max_length=4						,							truncation=lowerCAmelCase__						,							return_tensors='np'						,							return_attention_mask=lowerCAmelCase__						,							)
									_UpperCAmelCase							=		inputs.input_features
									_UpperCAmelCase							=		inputs.attention_mask
									_UpperCAmelCase							=		np.sum(attention_mask == 1						,							axis=1    )

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
									self._check_zero_mean_unit_variance(input_features[1]    )
									self._check_zero_mean_unit_variance(input_features[2]    )






							def 							lowerCamelCase_ (	self    )      ->				Tuple:
									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in range(800						,							1400						,							200    )]
									_UpperCAmelCase							=		feature_extractor(
									    lowerCAmelCase__						,							padding='longest'						,							max_length=4						,							truncation=lowerCAmelCase__						,							return_tensors='np'						,							return_attention_mask=lowerCAmelCase__						,							)
									_UpperCAmelCase							=		inputs.input_features
									_UpperCAmelCase							=		inputs.attention_mask
									_UpperCAmelCase							=		np.sum(attention_mask == 1						,							axis=1    )

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
									self._check_zero_mean_unit_variance(input_features[2]    )

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape						,							(3, 4, 24)    )

									_UpperCAmelCase							=		[floats_list((1, x)    )[0] for x in range(800						,							1400						,							200    )]
									_UpperCAmelCase							=		feature_extractor(
									    lowerCAmelCase__						,							padding='longest'						,							max_length=16						,							truncation=lowerCAmelCase__						,							return_tensors='np'						,							return_attention_mask=lowerCAmelCase__						,							)
									_UpperCAmelCase							=		inputs.input_features
									_UpperCAmelCase							=		inputs.attention_mask
									_UpperCAmelCase							=		np.sum(attention_mask == 1						,							axis=1    )

									self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
									self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
									self._check_zero_mean_unit_variance(input_features[2]    )

									# make sure that if max_length < longest -> then pad to max_length
									self.assertEqual(input_features.shape						,							(3, 6, 24)    )






							def 							lowerCamelCase_ (	self    )      ->				int:
									import torch

									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									_UpperCAmelCase							=		np.random.rand(100						,							32    ).astype(np.floataa    )
									_UpperCAmelCase							=		np_speech_inputs.tolist()

									for inputs in [py_speech_inputs, np_speech_inputs]:
											_UpperCAmelCase							=		feature_extractor.pad([{'input_features': inputs}]						,							return_tensors='np'    )
											self.assertTrue(np_processed.input_features.dtype == np.floataa    )
											_UpperCAmelCase							=		feature_extractor.pad([{'input_features': inputs}]						,							return_tensors='pt'    )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )






							def 							lowerCamelCase_ (	self						,							snake_case    )      ->				Optional[int]:
									from datasets import load_dataset

									_UpperCAmelCase							=		load_dataset('hf-internal-testing/librispeech_asr_dummy'						,							'clean'						,							split='validation'    )
									# automatic decoding with librispeech
									_UpperCAmelCase							=		ds.sort('id'    ).select(range(lowerCAmelCase__    )    )[:num_samples]['audio']

									return [x["array"] for x in speech_samples]






							def 							lowerCamelCase_ (	self    )      ->				Dict:
									_UpperCAmelCase							=		np.array([
									    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
									    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
									    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
									]    )
									# fmt: on

									_UpperCAmelCase							=		self._load_datasamples(1    )
									_UpperCAmelCase							=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
									_UpperCAmelCase							=		feature_extractor(lowerCAmelCase__						,							return_tensors='pt'    ).input_features
									self.assertEquals(input_features.shape						,							(1, 584, 24)    )
									self.assertTrue(np.allclose(input_features[0, 0, :30]						,							lowerCAmelCase__						,							atol=1E-4    )    )



def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				word.split()

				def justify(lowercase				,     lowercase				,     lowercase				) -> str:
								__lowercase					     =				max_width - width
								__lowercase					     =				len(lowercase				)
								if len(lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowercase					     =				words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowercase					     =				spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase					     =				(
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowercase				):
																num_spaces_between_words_list[i] += 1
												__lowercase					     =				[]
												for i in range(lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(lowercase				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				__lowercase					     =				0
				for word in words:
								if width + len(lowercase				) + len(lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowercase				)
												width += len(lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(lowercase				,     lowercase				,     lowercase				)				)
												# reset new line and new width
												__lowercase     ,	__lowercase					     =				[word], len(lowercase				)
				__lowercase					     =				max_width - width - len(lowercase				)
				answer.append(''' '''.join(lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()